| |
|
|
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import tensorflow as tf |
| from utils import dtype |
|
|
|
|
| def get_initializer(initializer, initializer_gain): |
| tfdtype = tf.as_dtype(dtype.floatx()) |
|
|
| if initializer == "uniform": |
| max_val = initializer_gain |
| return tf.random_uniform_initializer(-max_val, max_val, dtype=tfdtype) |
| elif initializer == "normal": |
| return tf.random_normal_initializer(0.0, initializer_gain, dtype=tfdtype) |
| elif initializer == "normal_unit_scaling": |
| return tf.variance_scaling_initializer(initializer_gain, |
| mode="fan_avg", |
| distribution="normal", |
| dtype=tfdtype) |
| elif initializer == "uniform_unit_scaling": |
| return tf.variance_scaling_initializer(initializer_gain, |
| mode="fan_avg", |
| distribution="uniform", |
| dtype=tfdtype) |
| else: |
| tf.logging.warn("Unrecognized initializer: %s" % initializer) |
| tf.logging.warn("Return to default initializer: glorot_uniform_initializer") |
| return tf.glorot_uniform_initializer(dtype=tfdtype) |
|
|
|
|
| def scale_initializer(scale, initializer): |
| """Rescale the value given by initializer""" |
| tfdtype = tf.as_dtype(dtype.floatx()) |
|
|
| def _initializer(shape, dtype=tfdtype, partition_info=None): |
| value = initializer(shape, dtype=dtype, partition_info=partition_info) |
| value *= scale |
|
|
| return value |
|
|
| return _initializer |
|
|
